Xavier Snelgrove
Editor at Orbital Studies Magazine
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolXavier Snelgrove is a multidisciplinary computer scientist and engineering leader with 12 years of experience turning ML and signal-processing research into production-grade systems that run in real time on millions of devices. He co-founded Whirlscape and led ML efforts behind Minuum and Dango, building language models and neural predictors for emoji, sticker and GIF suggestions, and later directed machine learning at Ro for healthcare applications. Equally at home in academia and industry, he has contributed to open-source projects like Keras and p5.js—improving RNN masking and rendering performance respectively—and taught creative coding through the GenArtHackParty and a residency at the University of Toronto. Xavier combines hands-on implementation across hardware-constrained environments with team leadership and product collaboration, and he mentors artists and engineers to view computation as a creative lens while staying mindful of model limitations.
12 years of coding experience
10 years of employment as a software developer
M.Sc. Computer Science, M.Sc. Computer Science at University of Toronto
English, French
Github Skills (17)
javascript10
artificial-neural-networks10
python10
machine-learning10
rnn-model10
p5js10
n10
graphic10
keras10
deeplearning-ai10
coding10
deep-learning10
tensorflow10
performance-optimization10
neural-network10
Programming languages (6)
Github contributions (5)
keras-team/keras
Jun 2015 - Feb 2016
Deep Learning for humans
Role in this project:
ML Engineer
ML EngineerContributions:53 commits, 19 PRs, 223 comments in 7 months
Contributions summary:Xavier made significant contributions to the `keras-team/keras` repository, primarily focused on improving the masking functionality within recurrent neural network layers. Their work included implementing masking in SimpleRNN, SimpleDeepRNN, GRU, and LSTM layers, as well as the TimeDistributedDense and Dropout layers. The user also fixed several bugs related to the correct application of masks and updated tests to verify the expected behavior of the masking implementation.
pythondata-sciencedeep-learningneural-networksmachine-learning
processing/p5.js
Jun 2017 - Jun 2017
p5.js is a client-side JS platform that empowers artists, designers, students, and anyone to learn to code and express themselves creatively on the web. It is based on the core principles of Processing. http://twitter.com/p5xjs —
Role in this project:Front-end Developer
Front-end DeveloperContributions:5 commits, 1 PR, 5 comments in 2 days
Contributions summary:Xavier primarily focused on optimizing the performance of the `p5.js` library's rendering engine. Their contributions involved caching frequently accessed styles (fill and stroke) to reduce redundant calls and improve drawing performance. They also refactored the code to ensure consistent use of caching mechanisms and updated test configurations, increasing test timeout values. These changes aimed to enhance the efficiency of the library's core drawing functions, impacting the user experience.
designersprinciplesartclient-sidecanvas
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